The process of contrast enhancement typically involves remapping the gray scale or intensity level of an image so that the image occupies all levels of available dynamic range. Until recently, contrast enhancement has been achieved by histogram equalization, where all available gray levels of a display have equal probability to be occupied by the pixels of an image. Histogram equalization, however, often over-stretches the distribution of pixels so that the resulting image has an artificial quality.
Another solution is contrast limited histogram equalization (CLHE), where exceptional peaks of a histogram are clipped to avoid their dominance in the equalized histogram. CLHE has several variations that consider the different sensitivities of visual perception at low, middle, and high levels. For example, CLHE can be done separately at different intensity regions if proper adjustments are made to ensure smooth transitions at the boundary of different regions. Setting the clipping thresholds so that the resulting image has the desired enhancement is difficult in CLHE as there are multiple thresholds. Human factors and biases play a role in the process, which makes it very subjective. Although algorithms have been proposed to estimate the thresholds automatically, they are not fully resolved. Consequently, CLHE is not a complete solution to the problem of contrast enhancement.